sRGB Gamut Clipping (2021)
The article addresses RGB gamut clipping in image processing, criticizing clamping methods and proposing two novel techniques using the Oklab color space to reduce color distortion while preserving lightness and chroma.
Read original articleThe article discusses the issue of RGB gamut clipping in image processing, where RGB values can exceed the valid range, leading to color distortion. It categorizes out-of-gamut colors into two types: those that are too bright and those that have negative values, which can represent colors that are not physically possible. The common method of clamping RGB values is criticized for causing significant color distortions. The article introduces two main research areas: tone mapping and gamut mapping, focusing specifically on the latter. Gamut clipping aims to map out-of-gamut colors back into the valid range without introducing artifacts. The author proposes using the Oklab perceptual color space to maintain hue while adjusting lightness and chroma. Two novel methods for gamut clipping are introduced: one that projects colors towards a midpoint and another that adapts based on the color's lightness. Both methods aim to preserve lightness while effectively managing chroma compression. The article concludes with a comparison of results from different gamut clipping techniques, highlighting the effectiveness of the proposed methods in reducing color distortion.
- RGB gamut clipping can lead to significant color distortions in image processing.
- Clamping RGB values is a common but flawed technique for handling out-of-gamut colors.
- The article focuses on developing practical alternatives to RGB clipping using the Oklab color space.
- Two novel gamut clipping methods are introduced, aiming to preserve lightness while managing chroma.
- The effectiveness of these methods is demonstrated through comparative results.
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https://github.com/texel-org/color
Many popular color libraries (Colorjs.io, culori) attempt to match CSS gamut mapping spec, which is an order of magnitude slower than the approach in Ottosson’s blog post, and also less accurate (CSS gamut mapping may not fall neatly on the gamut boundary).
[1] “Ported” might be a better term as I used a combination of Ottosson’s own JS OKHSL picker, Colorjs.io code, and Coloraide (Python), and adjusted it for performance, more gamuts, and smaller bundle sizes.
The exposure parameter is usually dynamically chosen by using the average brightness of a previous frame.
[1]: http://filmicworlds.com/blog/filmic-tonemapping-operators/
If you are a photographer, this process starts with camera settings at shooting time and ends with delivery to preferred display space. I believe the whole process can or even should be a creative one rather than purely algorithmic, and fitting dynamic range is an opportunity for you to accentuate/attenuate the right aspects of the image to convey a desirable effect.
(This is not unlike microphone placement in audio recording, or equalisation/compression in subsequent mixing. First you deal with extreme range of actual physical sound and limit it to capabilities of recording media, then you further fit it to the range of audio reproducing capabilities of consumer equipment and to modern listening conditions, and throughout the entire process you accentuate and attenuate.)
But even if you have a monitor that supports DCI-P3 you have to slog through modes, profiles, and blog posts to get it setup.
Should you always have HDR on? Why does SDR content always look "wrong" when HDR is on? Oh, it's because the peak brightness and color saturation blah blah blah.
Gamma 2.0, 2.2, or 2.4?
Now you learn the hard way the "desktop" is not color managed, it's the individual applications.. If they want to be. Maybe they'll use the windows configured profile, maybe not.
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